如何更改混淆矩阵中的刻度?
How to change the ticks in a confusion matrix?
我正在使用混淆矩阵(图 A)
如何让我的 ticks
从 1 到 3 而不是 0 到 2 开始?
我尝试在 tick_marks
中添加 +1。但是不行(图B)
检查我的代码:
import itertools
cm = confusion_matrix(y_test, y_pred)
np.set_printoptions(precision=2)
print('Confusion matrix, without normalization')
print(cm)
plt.figure()
plot_confusion_matrix(cm)
def plot_confusion_matrix(cm, title='Confusion matrix', cmap=plt.cm.Oranges):
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(iris.target_names)) + 1
plt.xticks(tick_marks, rotation=45)
plt.yticks(tick_marks)
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, cm[i, j],
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
图 A:
图B
您应该获取 plt
的 axis
并更改 xtick_labels
(如果您打算这样做):
import itertools
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
# import some data to play with
iris = datasets.load_iris()
X = iris.data
y = iris.target
class_names = iris.target_names
# Split the data into a training set and a test set
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
# Run classifier, using a model that is too regularized (C too low) to see
# the impact on the results
classifier = svm.SVC(kernel='linear', C=0.01)
y_pred = classifier.fit(X_train, y_train).predict(X_test)
def plot_confusion_matrix(cm, title='Confusion matrix', cmap=plt.cm.Oranges):
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(iris.target_names))
plt.xticks(tick_marks, rotation=45)
ax = plt.gca()
ax.set_xticklabels((ax.get_xticks() +1).astype(str))
plt.yticks(tick_marks)
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, cm[i, j],
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
cm = confusion_matrix(y_test, y_pred)
np.set_printoptions(precision=2)
print('Confusion matrix, without normalization')
print(cm)
fig, ax = plt.subplots()
plot_confusion_matrix(cm)
plt.show()
结果:
我遇到了类似的问题:当我想为我的 类 使用自定义标签时,方框超出范围或标签被偏移,如您在此处显示的那样。
如果您有多个标签 (>7),那么您首先需要使用 plticker.MultipleLocator 将刻度频率明确设置为一个。然后你只需设置 x 和 y 刻度标签而不提及刻度(不设置 xticks 和 yticks 很重要。如果你这样做, imshow/matshow 部分会在顶部被切掉。)在plot_confusion_matrix函数。
import matplotlib.ticker as plticker
fig = plt.figure()
ax = fig.add_subplot(111)
cax = ax.matshow(cm,cmap=cmap)
fig.colorbar(cax)
loc = plticker.MultipleLocator(base=1.0)
ax.xaxis.set_major_locator(loc)
ax.yaxis.set_major_locator(loc)
ax.set_yticklabels(['']+iris.target_names)
ax.set_xticklabels(['']+iris.target_names)
我正在使用混淆矩阵(图 A)
如何让我的 ticks
从 1 到 3 而不是 0 到 2 开始?
我尝试在 tick_marks
中添加 +1。但是不行(图B)
检查我的代码:
import itertools
cm = confusion_matrix(y_test, y_pred)
np.set_printoptions(precision=2)
print('Confusion matrix, without normalization')
print(cm)
plt.figure()
plot_confusion_matrix(cm)
def plot_confusion_matrix(cm, title='Confusion matrix', cmap=plt.cm.Oranges):
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(iris.target_names)) + 1
plt.xticks(tick_marks, rotation=45)
plt.yticks(tick_marks)
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, cm[i, j],
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
图 A:
图B
您应该获取 plt
的 axis
并更改 xtick_labels
(如果您打算这样做):
import itertools
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
# import some data to play with
iris = datasets.load_iris()
X = iris.data
y = iris.target
class_names = iris.target_names
# Split the data into a training set and a test set
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
# Run classifier, using a model that is too regularized (C too low) to see
# the impact on the results
classifier = svm.SVC(kernel='linear', C=0.01)
y_pred = classifier.fit(X_train, y_train).predict(X_test)
def plot_confusion_matrix(cm, title='Confusion matrix', cmap=plt.cm.Oranges):
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(iris.target_names))
plt.xticks(tick_marks, rotation=45)
ax = plt.gca()
ax.set_xticklabels((ax.get_xticks() +1).astype(str))
plt.yticks(tick_marks)
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, cm[i, j],
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
cm = confusion_matrix(y_test, y_pred)
np.set_printoptions(precision=2)
print('Confusion matrix, without normalization')
print(cm)
fig, ax = plt.subplots()
plot_confusion_matrix(cm)
plt.show()
结果:
我遇到了类似的问题:当我想为我的 类 使用自定义标签时,方框超出范围或标签被偏移,如您在此处显示的那样。
如果您有多个标签 (>7),那么您首先需要使用 plticker.MultipleLocator 将刻度频率明确设置为一个。然后你只需设置 x 和 y 刻度标签而不提及刻度(不设置 xticks 和 yticks 很重要。如果你这样做, imshow/matshow 部分会在顶部被切掉。)在plot_confusion_matrix函数。
import matplotlib.ticker as plticker
fig = plt.figure()
ax = fig.add_subplot(111)
cax = ax.matshow(cm,cmap=cmap)
fig.colorbar(cax)
loc = plticker.MultipleLocator(base=1.0)
ax.xaxis.set_major_locator(loc)
ax.yaxis.set_major_locator(loc)
ax.set_yticklabels(['']+iris.target_names)
ax.set_xticklabels(['']+iris.target_names)